Open Access

ARTICLE

Three Dimensional Optimum Node Localization in Dynamic Wireless Sensor Networks

by Gagandeep Singh Walia¹, Parulpreet Singh¹, Manwinder Singh¹, Mohamed Abouhawwash^2,3, Hyung Ju Park⁴, Byeong-Gwon Kang^4,*, Shubham Mahajan⁵, Amit Kant Pandit⁵

1 Department of Electronics and Communication Engineering, Lovely Professional University, Jalandhar, 144411, Punjab, India
2 Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
3 Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, MI, 48824, USA
4 Department of ICT Convergence, Soonchunhyang University, Asan, 31538, Korea
5 School of Electronics & Communication, Shri Mata Vaishno Devi University, Katra, 182320, India

* Corresponding Author: Byeong-Gwon Kang. Email:

Computers, Materials & Continua 2022, 70(1), 305-321. https://doi.org/10.32604/cmc.2022.019171

Received 03 April 2021; Accepted 04 May 2021; Issue published 07 September 2021

Abstract

Location information plays an important role in most of the applications in Wireless Sensor Network (WSN). Recently, many localization techniques have been proposed, while most of these deals with two Dimensional applications. Whereas, in Three Dimensional applications the task is complex and there are large variations in the altitude levels. In these 3D environments, the sensors are placed in mountains for tracking and deployed in air for monitoring pollution level. For such applications, 2D localization models are not reliable. Due to this, the design of 3D localization systems in WSNs faces new challenges. In this paper, in order to find unknown nodes in Three-Dimensional environment, only single anchor node is used. In the simulation-based environment, the nodes with unknown locations are moving at middle & lower layers whereas the top layer is equipped with single anchor node. A novel soft computing technique namely Adaptive Plant Propagation Algorithm (APPA) is introduced to obtain the optimized locations of these mobile nodes. These mobile target nodes are heterogeneous and deployed in an anisotropic environment having an Irregularity (Degree of Irregularity (DOI)) value set to 0.01. The simulation results present that proposed APPA algorithm outperforms as tested among other meta-heuristic optimization techniques in terms of localization error, computational time, and the located sensor nodes.

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Citations